@inproceedings{f3bc137e00254f058b612f3895e0320d,
title = "Extremely Compact Integrate-and-Fire STT-MRAM Neuron: A Pathway toward All-Spin Artificial Deep Neural Network",
abstract = "This work reports the complete framework from device to architecture for deep learning acceleration in an all-spin artificial neural network (ANN) built by highly manufacturable STT-MRAM technology. The most compact analog integrate-and-fire neuron reported to date is developed based on the back-hopping oscillation in magnetic tunnel junctions. This novel device is unique because it performs numerous essential neural functions simultaneously, including current integration, voltage spike generation, state reset, and 4-bit precision. The device itself is also a stochastic binary synapse, and thus eases the implementation of the compact all-spin ANN with high accuracy for online training.",
author = "Wu, {Ming Hung} and Hong, {Ming Chun} and Chang, {Chih Cheng} and Paritosh Sahu and Wei, {Jeng Hua} and Lee, {Heng Yuan} and Shcu, {Shyh Shyuan} and Tuo-Hung Hou",
year = "2019",
month = jun,
doi = "10.23919/VLSIT.2019.8776569",
language = "English",
series = "Digest of Technical Papers - Symposium on VLSI Technology",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "T34--T35",
booktitle = "2019 Symposium on VLSI Technology, VLSI Technology 2019 - Digest of Technical Papers",
address = "United States",
note = "null ; Conference date: 09-06-2019 Through 14-06-2019",
}